Its funny - I was going to leave a similar comment - and I have, earlier, on a different thread. If people need fast classification, on a fairly scoped problem, it is very fruitful to start with an off-the-shelf embedding model like ModernBERT (which Laya uses) and stick a classifier in front - like a Support Vector Machine (SVM). For starters just tune the SVM, you don't even have to fine-tune the embedder - often it works very well, esp. given the compute needed. Plus you can get reliable confidence scores and generate explanations if you want them (using something like SHAP).

If you have an easy problem that can be solved by a classifier from pre LLM era, then sure, go ahead. But we have LLMs now and we can use those to expensively and slowly classify harder problems using general purpose models, without needing to spend a huge amount of time fine tuning a model for the specific task. Jev offers to do the same fast and cheap.

For clarity: no, a 0.8B model is not gonna do that.